Treatment-specific changes in gene expression discriminate in vivo drug response in human leukemia cells

Early changes in gene expression profiles in AML patients during induction chemotherapy

Background

Elucidation of the genetic mechanisms underlying treatment response to standard induction chemotherapy in AML patients is warranted, in order to aid in risk-adapted treatment decisions as novel treatments are emerging. In this pilot study, we explored the treatment-induced expression patterns in a small cohort of AML patients by analyzing differential gene expression (DGE) over the first 2 days of induction chemotherapy.

Methods

Blood samples were collected from ten AML patients at baseline (before treatment initiation) and during the first 2 days of treatment (Day 1; approximately 24 h, and Day 2; approximately 48 h after treatment initiation, respectively) and RNA was extracted for subsequent RNA sequencing. DGE between time points were assessed by pairwise analysis using the R package edgeR version 3.18.1 in all patients as well as in relation to treatment response (complete remission, CR, vs non-complete remission, nCR). Ingenuity Pathway Analysis (Qiagen) software was used for pathway analysis and visualization.

Results

After initial data quality control, two patients were excluded from further analysis, resulting in a final cohort of eight patients with data from all three timepoints. DGE analysis demonstrated activation of pathways with genes directly or indirectly associated with NF-κB signaling. Significant activation of the NF-κB pathway was seen in 50% of the patients 2 days after treatment start, while iNOS pathway effects could be identified already after 1 day. nCR patients displayed activation of pathways associated with cell cycle progression, oncogenesis and anti-apoptotic behavior, including the STAT3 pathway and Salvage pathways of pyrimidine ribonucleotides. Notably, a significant induction of cytidine deaminase, an enzyme responsible for the deamination of Ara-C, could be observed between baseline and Day 2 in the nCR patients but not in patients achieving CR.

Conclusions

In conclusion, we show that time-course analysis of gene expression represents a feasible approach to identify relevant pathways affected by standard induction chemotherapy in AML patients. This poses as a potential method for elucidating new drug targets and biomarkers for categorizing disease aggressiveness and evaluating treatment response. However, more studies on larger cohorts are warranted to elucidate the transcriptional basis for drug response.

Bayesian Gene Selection Based on Pathway Information and Network-Constrained Regularization

High-throughput data make it possible to study expression levels of thousands of genes simultaneously under a particular condition. However, only few of the genes are discriminatively expressed. How to identify these biomarkers precisely is significant for disease diagnosis, prognosis, and therapy. Many studies utilized pathway information to identify the biomarkers. However, most of these studies only incorporate the group information while the pathway structural information is ignored. In this paper, we proposed a Bayesian gene selection with a network-constrained regularization method, which can incorporate the pathway structural information as priors to perform gene selection. All the priors are conjugated; thus, the parameters can be estimated effectively through Gibbs sampling. We present the application of our method on 6 microarray datasets, comparing with Bayesian Lasso, Bayesian Elastic Net, and Bayesian Fused Lasso. The results show that our method performs better than other Bayesian methods and pathway structural information can improve the result.

Environment-mediated drug resistance in Bcr/Abl-positive acute lymphoblastic leukemia

Although cure rates for acute lymphoblastic leukemia (ALL) have increased, development of resistance to drugs and patient relapse are common. The environment in which the leukemia cells are present during the drug treatment is known to provide significant survival benefit. Here, we have modeled this process by culturing murine Bcr/Abl-positive acute lymphoblastic leukemia cells in the presence of stroma while treating them with a moderate dose of two unrelated drugs, the farnesyltransferase inhibitor lonafarnib and the tyrosine kinase inhibitor nilotinib. This results in an initial large reduction in cell viability of the culture and inhibition of cell proliferation. However, after a number of days, cell death ceases and the culture becomes drug-tolerant, enabling cell division to resume. Using gene expression profiling, we found that the development of drug resistance was accompanied by massive transcriptional upregulation of genes that are associated with general inflammatory responses such as the metalloproteinase MMP9. MMP9 protein levels and enzymatic activity were also increased in ALL cells that had become nilotinib-tolerant. Activation of p38, Akt and Erk correlated with the development of environment-mediated drug resistance (EMDR), and inhibitors of Akt and Erk in combination with nilotinib reduced the ability of the cells to develop resistance. However, inhibition of p38 promoted increased resistance to nilotinib. We conclude that development of EMDR by ALL cells involves changes in numerous intracellular pathways. Development of tolerance to drugs such as nilotinib may therefore be circumvented by simultaneous treatment with other drugs having divergent targets.

Multi-view feature selection for identifying gene markers: a diversified biological data driven approach

BACKGROUND

In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population.

RESULTS

In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select. Three important resources of biological data (gene ontology, protein interaction data, protein sequence) along with gene expression values are collectively utilized to design two different views. UMVMO-select aims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets.

CONCLUSION

A thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting.

Single cell characterization of B-lymphoid differentiation and leukemic cell states during chemotherapy in ETV6-RUNX1-positive pediatric leukemia identifies drug-targetable transcription factor activities

BACKGROUND

Tight regulatory loops orchestrate commitment to B cell fate within bone marrow. Genetic lesions in this gene regulatory network underlie the emergence of the most common childhood cancer, acute lymphoblastic leukemia (ALL). The initial genetic hits, including the common translocation that fuses ETV6 and RUNX1 genes, lead to arrested cell differentiation. Here, we aimed to characterize transcription factor activities along the B-lineage differentiation trajectory as a reference to characterize the aberrant cell states present in leukemic bone marrow, and to identify those transcription factors that maintain cancer-specific cell states for more precise therapeutic intervention.

METHODS

We compared normal B-lineage differentiation and in vivo leukemic cell states using single cell RNA-sequencing (scRNA-seq) and several complementary genomics profiles. Based on statistical tools for scRNA-seq, we benchmarked a workflow to resolve transcription factor activities and gene expression distribution changes in healthy bone marrow lymphoid cell states. We compared these to ALL bone marrow at diagnosis and in vivo during chemotherapy, focusing on leukemias carrying the ETV6-RUNX1 fusion.

RESULTS

We show that lymphoid cell transcription factor activities uncovered from bone marrow scRNA-seq have high correspondence with independent ATAC- and ChIP-seq data. Using this comprehensive reference for regulatory factors coordinating B-lineage differentiation, our analysis of ETV6-RUNX1-positive ALL cases revealed elevated activity of multiple ETS-transcription factors in leukemic cells states, including the leukemia genome-wide association study hit ELK3. The accompanying gene expression changes associated with natural killer cell inactivation and depletion in the leukemic immune microenvironment. Moreover, our results suggest that the abundance of G1 cell cycle state at diagnosis and lack of differentiation-associated regulatory network changes during induction chemotherapy represent features of chemoresistance. To target the leukemic regulatory program and thereby overcome treatment resistance, we show that inhibition of ETS-transcription factors reduced cell viability and resolved pathways contributing to this using scRNA-seq.

CONCLUSIONS

Our data provide a detailed picture of the transcription factor activities characterizing both normal B-lineage differentiation and those acquired in leukemic bone marrow and provide a rational basis for new treatment strategies targeting the immune microenvironment and the active regulatory network in leukemia.

Exploring two-dimensional graphene and boron-nitride as potential nanocarriers for cytarabine and clofarabine anti-cancer drugs

Development in two-dimensional (2D) drug-delivery materials have quickly translated into biological and pharmacological fields. In this present work, pristine graphene (PG) and hexagonal boron nitride (h-BN) sheets are explored as a drug carrier for cytarabine (CYT) and clofarabine (CLF) anti-cancer drugs using density functional theory (DFT). The obtained geometrical, energetic and electronic properties revealed that the PG sheet is more reactive and it adsorbs CYT and CLF anti-cancer drugs better than the h-BN sheet. The adsorption energies of CYT and CLF on PG sheet is -24.293 and -23.308 kcal/mol respectively, this is due to the delocalized electrons present in the PG sheet. The flow of electron direction between anti-cancer drugs and 2D sheet are calculated by ΔN, ΔE_A(B), and ΔE_B(A) parameters and Natural bond orbital analysis (NBO). The electronic and optical properties are calculated to understand the chemical reactivity and stability of the complex systems. The obtained results exhibit that the PG sheet retains significant therapeutic potential as a drug delivery vehicle for a drug molecule to treat cancer therapy.

Evaluation of gene-drug common module identification methods using pharmacogenomics data

Accurately identifying the interactions between genomic factors and the response of cancer drugs plays important roles in drug discovery, drug repositioning and cancer treatment. A number of studies revealed that interactions between genes and drugs were 'many-genes-to-many drugs' interactions, i.e. common modules, opposed to 'one-gene-to-one-drug' interactions. Such modules fully explain the interactions between complex biological regulatory mechanisms and cancer drugs. However, strategies for effectively and robustly identifying the underlying common modules among pharmacogenomics data remain to be improved. In this paper, we aim to provide a detailed evaluation of three categories of state-of-the-art common module identification techniques from a machine learning perspective, including non-negative matrix factorization (NMF), partial least squares (PLS) and network analyses. We first evaluate the performance of six methods, namely SNMNMF, NetNMF, SNPLS, O2PLS, NSBM and HOGMMNC, using two series of simulated data sets with different noise levels and outlier ratios. Then, we conduct experiments using a real world data set of 2091 genes and 101 drugs in 392 cancer cell lines and compare the real experimental results from the aspect of biological process term enrichment, gene-drug and drug-drug interactions. Finally, we present interesting findings from our evaluation study and discuss the advantages and drawbacks of each method. Supplementary information: Supplementary file is available at Briefings in Bioinformatics online.

Multi spectral classification and recognition of breast cancer and pneumonia

According to the Google I/O 2018 key notes, in future artificial intelligence, which also includes machine learning and deep learning, will mostly evolve in healthcare domain. As there are lots of subdomains which come under the category of healthcare domain, the proposed paper concentrates on one such domain, that is breast cancer and pneumonia. Today, just classifying the diseases is not enough. The system should also be able to classify a particular patient’s disease. Thus, this paper shines the light on the importance of multi spectral classification which means the collection of several monochrome images of the same scene. It can be proved to be an important process in the healthcare areas to know if a patient is suffering from a specific disease or not. The convolutional layer followed by the pooling layer is used for the feature extraction process and for the classification process; fully connected layers followed by the regression layer are used.

Integrative genomic analyses reveal mechanisms of glucocorticoid resistance in acute lymphoblastic leukemia

Identification of genomic and epigenomic determinants of drug resistance provides important insights for improving cancer treatment. Using agnostic genome-wide interrogation of mRNA and miRNA expression, DNA methylation, SNPs, CNAs and SNVs/Indels in primary human acute lymphoblastic leukemia cells, we identified 463 genomic features associated with glucocorticoid resistance. Gene-level aggregation identified 118 overlapping genes, 15 of which were confirmed by genome-wide CRISPR screen. Collectively, this identified 30 of 38 (79%) known glucocorticoid-resistance genes/miRNAs and all 38 known resistance pathways, while revealing 14 genes not previously associated with glucocorticoid-resistance. Single cell RNAseq and network-based transcriptomic modelling corroborated the top previously undiscovered gene, CELSR2. Manipulation of CELSR2 recapitulated glucocorticoid resistance in human leukemia cell lines and revealed a synergistic drug combination (prednisolone and venetoclax) that mitigated resistance in mouse xenograft models. These findings illustrate the power of an integrative genomic strategy for elucidating genes and pathways conferring drug resistance in cancer cells.

A survey of neural network-based cancer prediction models from microarray data

Neural networks are powerful tools used widely for building cancer prediction models from microarray data. We review the most recently proposed models to highlight the roles of neural networks in predicting cancer from gene expression data. We identified articles published between 2013-2018 in scientific databases using keywords such as cancer classification, cancer analysis, cancer prediction, cancer clustering and microarray data. Analyzing the studies reveals that neural network methods have been either used for filtering (data engineering) the gene expressions in a prior step to prediction; predicting the existence of cancer, cancer type or the survivability risk; or for clustering unlabeled samples. This paper also discusses some practical issues that can be considered when building a neural network-based cancer prediction model. Results indicate that the functionality of the neural network determines its general architecture. However, the decision on the number of hidden layers, neurons, hypermeters and learning algorithm is made using trail-and-error techniques.

Association of DFNA5, SYK, and NELL1 variants along with HPV infection in oral cancer among the prolonged tobacco-chewers

Southeast Asia, especially India, is well known for the highest use of smokeless tobacco. These products are known to induce oral squamous cell carcinoma. However, not all long-term tobacco-chewers develop oral squamous cell carcinoma. In addition, germline variants play a crucial role in susceptibility, prognosis, development, and progression of the disease. These prompted us to study the genetic susceptibility to oral squamous cell carcinoma among the long-term tobacco-chewers. Here, we presented a retrospective study on prolonged tobacco-chewers of Northeast India to identify the potential protective or risk-associated germline variants in tobacco-related oral squamous cell carcinoma along with HPV infection. Targeted re-sequencing (n = 60) of 170 genetic regions from 75 genes was carried out in Ion-PGM™ and validation (n = 116) of the observed variants was done using Sequenom iPLEX MassARRAY™ platform followed by polymerase chain reaction-based HPV genotyping and p16-immunohistochemistry study. Subsequently, estimation of population structure, different statistical and in silico approaches were undertaken. We identified one nonsense-mediated mRNA decay transcript variant in the DFNA5 region (rs2237306), associated with Benzo(a)pyrene, as a protective factor (odds ratio = 0.33; p = 0.009) and four harmful (odds ratio > 2.5; p < 0.05) intronic variants, rs182361, rs290974, and rs169724 in SYK and rs1670661 in NELL1 region, involved in genetic susceptibility to tobacco- and HPV-mediated oral oncogenesis. Among the oral squamous cell carcinoma patients, 12.6% (11/87) were HPV positive, out of which 45.5% (5/11) were HPV16-infected, 27.3% (3/11) were HPV18-infected, and 27.3% (3/11) had an infection of both subtypes. Multifactor dimensionality reduction analysis showed that the interactions among HPV and NELL1 variant rs1670661 with age and gender augmented the risk of both non-tobacco- and tobacco-related oral squamous cell carcinoma, respectively. These suggest that HPV infection may be one of the important risk factors for oral squamous cell carcinoma in this population. Finally, we newly report a DFNA5 variant probably conferring protection via nonsense-mediated mRNA decay pathway against tobacco-related oral squamous cell carcinoma. Thus, the analytical approach used here can be useful in predicting the population-specific significant variants associated with oral squamous cell carcinoma in any heterogeneous population.

Tumor gene expression data classification via sample expansion-based deep learning

Since tumor is seriously harmful to human health, effective diagnosis measures are in urgent need for tumor therapy. Early detection of tumor is particularly important for better treatment of patients. A notable issue is how to effectively discriminate tumor samples from normal ones. Many classification methods, such as Support Vector Machines (SVMs), have been proposed for tumor classification. Recently, deep learning has achieved satisfactory performance in the classification task of many areas. However, the application of deep learning is rare in tumor classification due to insufficient training samples of gene expression data. In this paper, a Sample Expansion method is proposed to address the problem. Inspired by the idea of Denoising Autoencoder (DAE), a large number of samples are obtained by randomly cleaning partially corrupted input many times. The expanded samples can not only maintain the merits of corrupted data in DAE but also deal with the problem of insufficient training samples of gene expression data to a certain extent. Since Stacked Autoencoder (SAE) and Convolutional Neural Network (CNN) models show excellent performance in classification task, the applicability of SAE and 1-dimensional CNN (1DCNN) on gene expression data is analyzed. Finally, two deep learning models, Sample Expansion-Based SAE (SESAE) and Sample Expansion-Based 1DCNN (SE1DCNN), are designed to carry out tumor gene expression data classification by using the expanded samples. Experimental studies indicate that SESAE and SE1DCNN are very effective in tumor classification.

EgoNet identifies differential ego-modules and pathways related to prednisolone resistance in childhood acute lymphoblastic leukemia

PURPOSE

To extract feature ego-modules and pathways in childhood acute lymphoblastic leukemia (ALL) resistant to prednisolone treatment, and further to explore the mechanisms behind prednisolone resistance.

MATERIALS AND METHODS

EgoNet algorithm was used to identify candidate ego-network modules, mainly via constructing differential co-expression network (DCN); selecting ego genes; collecting ego-network modules; refining candidate modules. Afterwards, statistical significance was calculated for these candidate modules. Biological functions of differential ego-network modules were identified using Reactome database. To verify this proposed method can lead to truly positive findings in clinical settings, support vector machine (SVM) was utilized to compute the AUC values for each significant pathway using 3-fold cross-validation method. To predict the reliability of our findings, another established method (attract) was used to identify the differential attractor modules using the same microarray profile.

RESULTS

After eliminating the modules with classification accuracy < 0.9 and node number < 15, only ego-network module 30 was eligible. After significance calculation, module 30 was significant. Module 30 was enriched in APC/C-mediated degradation of cell cycle proteins. The AUC for the significant pathway of APC/C-mediated degradation of cell cycle proteins was 0.915. Although the attract method obtained more modules, the module identified by our proposed method owned more gene nodes, and had more classification ability (AUC = 0.915).

CONCLUSION

One differential ego-network module identified in childhood ALL resistance to prednisolone based on DCN and EgoNet, might be helpful to reveal the mechanisms underlying prednisolone resistance in childhood ALL.

Suppressors and activators of JAK-STAT signaling at diagnosis and relapse of acute lymphoblastic leukemia in Down syndrome

Children with Down syndrome (DS) are prone to development of high-risk B-cell precursor ALL (DS-ALL), which differs genetically from most sporadic pediatric ALLs. Increased expression of cytokine receptor-like factor 2 (CRLF2), the receptor to thymic stromal lymphopoietin (TSLP), characterizes about half of DS-ALLs and also a subgroup of sporadic "Philadelphia-like" ALLs. To understand the pathogenesis of relapsed DS-ALL, we performed integrative genomic analysis of 25 matched diagnosis-remission and -relapse DS-ALLs. We found that the CRLF2 rearrangements are early events during DS-ALL evolution and generally stable between diagnoses and relapse. Secondary activating signaling events in the JAK-STAT/RAS pathway were ubiquitous but highly redundant between diagnosis and relapse, suggesting that signaling is essential but that no specific mutations are "relapse driving." We further found that activated JAK2 may be naturally suppressed in 25% of CRLF2^pos DS-ALLs by loss-of-function aberrations in USP9X, a deubiquitinase previously shown to stabilize the activated phosphorylated JAK2. Interrogation of large ALL genomic databases extended our findings up to 25% of CRLF2^pos, Philadelphia-like ALLs. Pharmacological or genetic inhibition of USP9X, as well as treatment with low-dose ruxolitinib, enhanced the survival of pre-B ALL cells overexpressing mutated JAK2. Thus, somehow counterintuitive, we found that suppression of JAK-STAT "hypersignaling" may be beneficial to leukemic B-cell precursors. This finding and the reduction of JAK mutated clones at relapse suggest that the therapeutic effect of JAK specific inhibitors may be limited. Rather, combined signaling inhibitors or direct targeting of the TSLP receptor may be a useful therapeutic strategy for DS-ALL.

D-Map: Random Walking on Gene Network Inference Maps Towards differential Avenue Discovery

Differential rewiring of cellular interaction networks between disease and healthy state is of great importance. Through a systems level approach, malfunctioned mechanisms that are absent in the normal cases, may enlighten the key-players in terms of genes and their interaction chains related to disease. We have developed D-Map, a publicly available user-friendly web application, capable of generating and manipulating advanced differential networks by combining state-of-the-art inference reconstruction methods with random walk simulations. The inputs are expression profiles obtained from the Gene Expression Omnibus and a gene list under investigation. Differential networks may be visualized and interpreted through the use of D-Map interface, where display of the disease, the normal and the common state can be performed, interactively. A case study scenario concerning Alzheimer's disease, as well as breast, lung, and bladder cancer was conducted in order to demonstrate the usefulness of the proposed methodology to different disease types. Findings were consistent with the current bibliography, and the provided interaction lists may be further explored towards novel biological insights of the investigated diseases. The DMap web-application is available at: http://bioserver-3.bioacademy.gr/Bioserver/DMap/index.php.

High-Dimensional Profiling for Computational Diagnosis

New technologies allow for high-dimensional profiling of patients. For instance, genome-wide gene expression analysis in tumors or in blood is feasible with microarrays, if all transcripts are known, or even without this restriction using high-throughput RNA sequencing. Other technologies like NMR finger printing allow for high-dimensional profiling of metabolites in blood or urine. Such technologies for high-dimensional patient profiling represent novel possibilities for molecular diagnostics. In clinical profiling studies, researchers aim to predict disease type, survival, or treatment response for new patients using high-dimensional profiles. In this process, they encounter a series of obstacles and pitfalls. We review fundamental issues from machine learning and recommend a procedure for the computational aspects of a clinical profiling study.

Efficacy of olanzapine in symptom relief and quality of life in gastric cancer patients receiving chemotherapy

Background:

Considering the incidence and prevalence rates of gastric cancer in Mazandaran Province of Iran, this research was performed to evaluate the efficacy and safety of olanzapine in symptom relief and quality of life (QOL) improvement of gastric patients receiving chemotherapy.

Materials and Methods:

This clinical trial was conducted on thirty new cases of gastric cancer patients whose treatment protocol was planned on chemotherapy and were allocated into two groups by simple random sampling. Intervention group (15 patients) received olanzapine tablets (2.5–10 mg/day) a day before the beginning of chemotherapy; in the 1^st day of chemotherapy to 8 weeks after chemotherapy, besides the routine treatment regimens. The control group received only the routine treatment regimens. The patients were followed for 8 weeks after intervention. All of the patients were assessed with Hospital Anxiety and Depression Scale (HADS) and WHO-QOL-BREF questionnaires; further, Rhodes index was used to evaluate nausea and vomiting (N/V) status.

Results:

All the recruited patients continued the allocated interventions (no lost to follow-up). N/V decreased in the case group, but the difference was not statistically significant (P = 0.438). The patients' appetite and body mass index increased (P = 0.006). Anxiety and depression subscales of HADS had significant differences between the two groups (P < 0.001) in the 4^th and 8^th week after treatment. Among the different subdomains of QOL, only physical health improved significantly after intervention (P < 0.05), but no significant difference was observed in other subdomains and also total QOL score (P > 0.05). No significant increase was observed in fasting and 2-h postprandial blood glucose and lipid profile (P > 0.05).

Conclusion:

Olanzapine can be considered as an effective drug to increase appetite and decrease anxiety and depression in patients with gastric cancer.

Genomic and transcriptomic profiles and in vitro resistance to mitoxantrone and idarubicin in pediatric acute leukemias

BACKGROUND

A major problem in the treatment of leukemia is the development of drug resistance to chemotherapeutic agents.

METHODS

To determine the ex vivo drug resistance profile to anthracyclines, an 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazoliumbromide (MTT) cytotoxicity assay was performed on mononuclear cells obtained from 155 patients with acute lymphoblastic leukemia (ALL) or acute myeloblastic leukemia (AML). Gene expression profiles (for 51 patients with ALL and 16 with AML) were prepared on the basis of cRNA hybridization to oligonucleotide arrays of the human genome (Affymetrix). Hierarchical clustering, assignment location and biological function were investigated during the correlation analysis for identified probe sets. Comparative genomic hybridization (CGH) array profiles (34 patients with ALL and 12 with AML) were prepared on the basis of DNA hybridization to oligonucleotide arrays of the human genome (Agilent). The validation of the array results was performed by a quantitative reverse transcriptase polymerase chain reaction.

RESULTS

The collected expression and CGH microarray experiment results indicate that the ITGB2, SCL6A7, CASP1 and DUSP genes may comprise a resistance marker for acute leukemia cells correlated with anthracyclines. Moreover, there were also identified chromosome rearrangements associated with drug resistance, such as del5q32-35.3 and amp8p12-p11.21. Precise genes, as well as genome aberrations, might be classified as targets in therapy.

CONCLUSIONS

In AML, the resistance of blasts to idarubicin and mitoxantrone may reflect an impaired integrin pathway. In ALL, the development of resistance is caused by the inhibition of B and T cell activation. Copyright © 2016 John Wiley & Sons, Ltd.

Drugs that reverse disease transcriptomic signatures are more effective in a mouse model of dyslipidemia

High-throughput omics have proven invaluable in studying human disease, and yet day-to-day clinical practice still relies on physiological, non-omic markers. The metabolic syndrome, for example, is diagnosed and monitored by blood and urine indices such as blood cholesterol levels. Nevertheless, the association between the molecular and the physiological manifestations of the disease, especially in response to treatment, has not been investigated in a systematic manner. To this end, we studied a mouse model of diet-induced dyslipidemia and atherosclerosis that was subject to various drug treatments relevant to the disease in question. Both physiological data and gene expression data (from the liver and white adipose) were analyzed and compared. We find that treatments that restore gene expression patterns to their norm are associated with the successful restoration of physiological markers to their baselines. This holds in a tissue-specific manner—treatments that reverse the transcriptomic signatures of the disease in a particular tissue are associated with positive physiological effects in that tissue. Further, treatments that introduce large non-restorative gene expression alterations are associated with unfavorable physiological outcomes. These results provide a sound basis to in silico methods that rely on omic metrics for drug repurposing and drug discovery by searching for compounds that reverse a disease's omic signatures. Moreover, they highlight the need to develop drugs that restore the global cellular state to its healthy norm rather than rectify particular disease phenotypes.

MicroRNAs Form Triplexes with Double Stranded DNA at Sequence-Specific Binding Sites; a Eukaryotic Mechanism via which microRNAs Could Directly Alter Gene Expression

MicroRNAs are important regulators of gene expression, acting primarily by binding to sequence-specific locations on already transcribed messenger RNAs (mRNA) and typically down-regulating their stability or translation. Recent studies indicate that microRNAs may also play a role in up-regulating mRNA transcription levels, although a definitive mechanism has not been established. Double-helical DNA is capable of forming triple-helical structures through Hoogsteen and reverse Hoogsteen interactions in the major groove of the duplex, and we show physical evidence (i.e., NMR, FRET, SPR) that purine or pyrimidine-rich microRNAs of appropriate length and sequence form triple-helical structures with purine-rich sequences of duplex DNA, and identify microRNA sequences that favor triplex formation. We developed an algorithm (Trident) to search genome-wide for potential triplex-forming sites and show that several mammalian and non-mammalian genomes are enriched for strong microRNA triplex binding sites. We show that those genes containing sequences favoring microRNA triplex formation are markedly enriched (3.3 fold, p<2.2 × 10⁻¹⁶) for genes whose expression is positively correlated with expression of microRNAs targeting triplex binding sequences. This work has thus revealed a new mechanism by which microRNAs could interact with gene promoter regions to modify gene transcription.

We provide physical evidence, using NMR, FRET and SPR, that purine or pyrimidine-rich microRNAs can form triplexes with complementary purine-rich sequences of duplex DNA and provide an algorithm (Trident) to search genome-wide for potential microRNA double-stranded DNA triplex-forming sites. Using this algorithm we document enrichment of microRNA triplex binding sites in mammalian and non-mammalian genomes. We found in primary leukemia cells from patients a significant over-representation of positively correlated microRNA and mRNA expression for genes containing sequences favoring microRNA-duplex DNA triplex formation, suggesting this as a mechanism by which microRNA may enhance gene transcription.

A Computational Pipeline for Cross-Species Analysis of RNA-seq Data Using R and Bioconductor

RNA sequencing (RNA-seq) has revolutionized transcriptome analysis through profiling the expression of thousands of genes at the same time. Systematic analysis of orthologous transcripts across species is critical for understanding the evolution of gene expression and uncovering important information in animal models of human diseases. Several computational methods have been published for analyzing gene expression between species, but they often lack crucial details and therefore cannot serve as a practical guide. Here, we present the first step-by-step protocol for cross-species RNA-seq analysis with a concise workflow that is largely based on the free open-source R language and Bioconductor packages. This protocol covers the entire process from short-read mapping, gene expression quantification, differential expression analysis to pathway enrichment. Many useful utilities for data visualization are included. This complete and easy-to-follow protocol provides hands-on guidance for users who are new to cross-species gene expression analysis.

Grouping of histone deacetylase inhibitors and other toxicants disturbing neural crest migration by transcriptional profiling

Functional assays, such as the "migration inhibition of neural crest cells" (MINC) developmental toxicity test, can identify toxicants without requiring knowledge on their mode of action (MoA). Here, we were interested, whether (i) inhibition of migration by structurally diverse toxicants resulted in a unified signature of transcriptional changes; (ii) whether statistically-identified transcript patterns would inform on compound grouping even though individual genes were little regulated, and (iii) whether analysis of a small group of biologically-relevant transcripts would allow the grouping of compounds according to their MoA. We analyzed transcripts of 35 'migration genes' after treatment with 16 migration-inhibiting toxicants. Clustering, principal component analysis and correlation analyses of the data showed that mechanistically related compounds (e.g. histone deacetylase inhibitors (HDACi), PCBs) triggered similar transcriptional changes, but groups of structurally diverse toxicants largely differed in their transcriptional effects. Linear discriminant analysis (LDA) confirmed the specific clustering of HDACi across multiple separate experiments. Similarity of the signatures of the HDACi trichostatin A and suberoylanilide hydroxamic acid to the one of valproic acid (VPA), suggested that the latter compound acts as HDACi when impairing neural crest migration. In conclusion, the data suggest that (i) a given functional effect (e.g. inhibition of migration) can be associated with highly diverse signatures of transcript changes; (ii) statistically significant grouping of mechanistically-related compounds can be achieved on the basis of few genes with small regulations. Thus, incorporation of mechanistic markers in functional in vitro tests may support read-across procedures, also for structurally un-related compounds.

Molecular learning with DNA kernel machines

We present a computational learning method for bio-molecular classification. This method shows how to design biochemical operations both for learning and pattern classification. As opposed to prior work, our molecular algorithm learns generic classes considering the realization in vitro via a sequence of molecular biological operations on sets of DNA examples. Specifically, hybridization between DNA molecules is interpreted as computing the inner product between embedded vectors in a corresponding vector space, and our algorithm performs learning of a binary classifier in this vector space. We analyze the thermodynamic behavior of these learning algorithms, and show simulations on artificial and real datasets as well as demonstrate preliminary wet experimental results using gel electrophoresis.

Multiclass cancer classification based on gene expression comparison

As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analyses, microarray-based cancer classification comprising multiple discriminatory molecular markers is an emerging trend. Such multiclass classification problems pose new methodological and computational challenges for developing novel and effective statistical approaches. In this paper, we introduce a new approach for classifying multiple disease states associated with cancer based on gene expression profiles. Our method focuses on detecting small sets of genes in which the relative comparison of their expression values leads to class discrimination. For an m-class problem, the classification rule typically depends on a small number of m-gene sets, which provide transparent decision boundaries and allow for potential biological interpretations. We first test our approach on seven common gene expression datasets and compare it with popular classification methods including support vector machines and random forests. We then consider an extremely large cohort of leukemia cancer patients to further assess its effectiveness. In both experiments, our method yields comparable or even better results to benchmark classifiers. In addition, we demonstrate that our approach can integrate pathway analysis of gene expression to provide accurate and biological meaningful classification.

NALP3 inflammasome upregulation and CASP1 cleavage of the glucocorticoid receptor cause glucocorticoid resistance in leukemia cells

Glucocorticoids are universally used in the treatment of acute lymphoblastic leukemia (ALL), and leukemia cell resistant to glucocorticoids confers a poor prognosis. To elucidate mechanisms of glucocorticoid resistance, we determined the sensitivity to prednisolone of primary leukemia cells from 444 newly diagnosed ALL patients, revealing significantly higher expression of caspase 1 (CASP1) and its activator NLRP3 in glucocorticoid resistant leukemia cells, due to significantly lower somatic methylation of CASP1 and NLRP3 promoters. Over-expression of CASP1 resulted in cleavage of the glucocorticoid receptor, diminished glucocorticoid-induced transcriptional response and increased glucocorticoid resistance. Knockdown or inhibition of CASP1 significantly increased glucocorticoid receptor levels and mitigated glucocorticoid resistance in CASP1 overexpressing ALL. Our findings establish a new mechanism by which the NLRP3/CASP1 inflammasome modulates cellular levels of the glucocorticoid receptor and diminishes cell sensitivity to glucocorticoids. The broad impact on glucocorticoid transcriptional response suggests this mechanism could also modify glucocorticoid effects in other diseases.

Identifying Non-Redundant Gene Markers from Microarray Data: A Multiobjective Variable Length PSO-Based Approach

Identifying relevant genes which are responsible for various types of cancer is an important problem. In this context, important genes refer to the marker genes which change their expression level in correlation with the risk or progression of a disease, or with the susceptibility of the disease to a given treatment. Gene expression profiling by microarray technology has been successfully applied to classification and diagnostic prediction of cancers. However, extracting these marker genes from a huge set of genes contained by the microarray data set is a major problem. Most of the existing methods for identifying marker genes find a set of genes which may be redundant in nature. Motivated by this, a multiobjective optimization method has been proposed which can find a small set of non-redundant disease related genes providing high sensitivity and specificity simultaneously. In this article, the optimization problem has been modeled as a multiobjective one which is based on the framework of variable length particle swarm optimization. Using some real-life data sets, the performance of the proposed algorithm has been compared with that of other state-of-the-art techniques.

A graph-theoretic approach for identifying non-redundant and relevant gene markers from microarray data using multiobjective binary PSO

The purpose of feature selection is to identify the relevant and non-redundant features from a dataset. In this article, the feature selection problem is organized as a graph-theoretic problem where a feature-dissimilarity graph is shaped from the data matrix. The nodes represent features and the edges represent their dissimilarity. Both nodes and edges are given weight according to the feature's relevance and dissimilarity among the features, respectively. The problem of finding relevant and non-redundant features is then mapped into densest subgraph finding problem. We have proposed a multiobjective particle swarm optimization (PSO)-based algorithm that optimizes average node-weight and average edge-weight of the candidate subgraph simultaneously. The proposed algorithm is applied for identifying relevant and non-redundant disease-related genes from microarray gene expression data. The performance of the proposed method is compared with that of several other existing feature selection techniques on different real-life microarray gene expression datasets.

Early gene expression of acute myeloid leukemia in response to chemotherapy

The use of gene expression arrays in the evaluation and classification of tumors is becoming increasingly important in a number of malignancies. This is a powerful technique able to disclose interpatient variance in gene expression. Such variation in gene expression may be the cause of different disease outcome and may reflect disease phenotypes or chemoresistance. Acute myeloid leukemia is a malignant disease of the bone marrow where overall long-term disease-free survival is less than 50%. The need for better disease classification and evaluation is consequently evident. Gene expression profiling in acute myeloid leukemia has, in recent years, proven able to distinguish acute myeloid leukemia subclasses and predict clinical outcome and is, as such, a promising technique for improved disease evaluation. The early detection of gene expression in response to chemotherapy may be a novel way of monitoring disease management. The immediate gene response may be an indication of whether the drug of choice is efficient in leukemic cell eradication and may early indicate the need for other therapeutic measures. Furthermore, these early alterations in gene expression could facilitate identification of new treatment targets, thereby enabling better patient care and follow-up in the future.

Use of gene expression microarrays for the study of acute leukemia

Genetic lesions found in acute leukemia drive the pathology of the disease in addition to forming reliable classifications of prognosis. However, there is still a reasonable heterogeneity of response among cases with the same genetic lesion. Moreover, many leukemia cases have no detectable genetic marker and these cases have marked heterogeneity of response. How can we learn more about the genes and pathways involved with leukemogenesis and response in the midst of such complexity? Gene expression microarrays are experimental platforms that allow for the simultaneous evaluation of the thousands of mRNA transcripts (the 'transcriptome'). This technology has revolutionized the study of leukemia, giving insight into genes and pathways involved in disease response and the biology involved in specific translocations.

6-thioguanine induces mitochondrial dysfunction and oxidative DNA damage in acute lymphoblastic leukemia cells

Thiopurines are among the most successful chemotherapeutic agents used for treating various human diseases, including acute lymphoblastic leukemia and chronic inflammation. Although metabolic conversion and the subsequent incorporation of 6-thioguanine ((S)G) nucleotides into nucleic acids are considered important for allowing the thiopurine drugs to induce their cytotoxic effects, alternative mechanisms may also exist. We hypothesized that an unbiased analysis of (S)G-induced perturbation of the entire proteome might uncover novel mechanism(s) of action of the drug. We performed a quantitative assessment of global protein expression in control and (S)G-treated Jurkat T cells by employing stable isotope labeling by amino acids in cell culture and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. LC-MS/MS quantification results uncovered substantially decreased expression of a large number of proteins in the mitochondrial respiratory chain complex, and Ingenuity Pathway Analysis of the significantly altered proteins showed that (S)G treatment induced mitochondrial dysfunction. This was accompanied by diminished uptake of MitoTracker Deep Red and the elevated formation of oxidatively induced DNA lesions, including 8,5'-cyclo-2'-deoxyadenosine and 8,5'-cyclo-2'-deoxyguanosine. Together, our results suggested that (S)G may exert its cytotoxic effect by inducing mitochondrial dysfunction and reactive oxygen species formation in acute lymphoblastic leukemia cells.

Comprehensive genomic characterization of cutaneous malignant melanoma cell lines derived from metastatic lesions by whole-exome sequencing and SNP array profiling

Cutaneous malignant melanoma is the most fatal skin cancer and although improved comprehension of its pathogenic pathways allowed to realize some effective molecular targeted therapies, novel targets and drugs are still needed. Aiming to add genetic information potentially useful for novel targets discovery, we performed an extensive genomic characterization by whole-exome sequencing and SNP array profiling of six cutaneous melanoma cell lines derived from metastatic patients. We obtained a total of 3,325 novel coding single nucleotide variants, including 2,172 non-synonymous variants. We catalogued the coding mutations according to Sanger COSMIC database and to a manually curated list including genes involved in melanoma pathways identified by mining recent literature. Besides confirming the presence of known melanoma driver mutations (BRAF(V600E), NRAS(Q61R) ), we identified novel mutated genes involved in signalling pathways crucial for melanoma pathogenesis and already addressed by current targeted therapies (such as MAPK and glutamate pathways). We also identified mutations in four genes (MUC19, PAICS, RBMXL1, KIF23) never reported in melanoma, which might deserve further investigations. All data are available to the entire research community in our Melanoma Exome Database (at https://155.253.6.64/MExDB/). In summary, these cell lines are valuable biological tools to improve the genetic comprehension of this complex cancer disease and to study functional relevance of individual mutational events, and these findings could provide insights potentially useful for identification of novel therapeutic targets for cutaneous malignant melanoma.

Linking proteins to signaling pathways for experiment design and evaluation

Biomedical experimental work often focuses on altering the functions of selected proteins. These changes can hit signaling pathways, and can therefore unexpectedly and non-specifically affect cellular processes. We propose PathwayLinker, an online tool that can provide a first estimate of the possible signaling effects of such changes, e.g., drug or microRNA treatments. PathwayLinker minimizes the users' efforts by integrating protein-protein interaction and signaling pathway data from several sources with statistical significance tests and clear visualization. We demonstrate through three case studies that the developed tool can point out unexpected signaling bias in normal laboratory experiments and identify likely novel signaling proteins among the interactors of known drug targets. In our first case study we show that knockdown of the Caenorhabditis elegans gene cdc-25.1 (meant to avoid progeny) may globally affect the signaling system and unexpectedly bias experiments. In the second case study we evaluate the loss-of-function phenotypes of a less known C. elegans gene to predict its function. In the third case study we analyze GJA1, an anti-cancer drug target protein in human, and predict for this protein novel signaling pathway memberships, which may be sources of side effects. Compared to similar services, a major advantage of PathwayLinker is that it drastically reduces the necessary amount of manual literature searches and can be used without a computational background. PathwayLinker is available at http://PathwayLinker.org. Detailed documentation and source code are available at the website.

A network-based maximum link approach towards MS identifies potentially important roles for undetected ARRB1/2 and ACTB in liver cancer progression

Hepatocellular Carcinoma (HCC) ranks among the deadliest of cancers and has a complex etiology. Proteomics analysis using iTRAQ provides a direct way to analyse perturbations in protein expression during HCC progression from early- to late-stage but suffers from consistency and coverage issues. Appropriate use of network-based analytical methods can help to overcome these issues. We built an integrated and comprehensive Protein-Protein Interaction Network (PPIN) by merging several major databases. Additionally, the network was filtered for GO coherent edges. Significantly differential genes (seeds) were selected from iTRAQ data and mapped onto this network. Undetected proteins linked to seeds (linked proteins) were identified and functionally characterised. The process of network cleaning provides a list of higher quality linked proteins, which are highly enriched for similar biological process gene ontology terms. Linked proteins are also enriched for known cancer genes and are linked to many well-established cancer processes such as apoptosis and immune response. We found that there is an increased propensity for known cancer genes to be found in highly linked proteins. Three highly-linked proteins were identified that may play an important role in driving HCC progression - the G-protein coupled receptor signalling proteins, ARRB1/2 and the structural protein beta-actin, ACTB. Interestingly, both ARRB proteins evaded detection in the iTRAQ screen. ACTB was not detected in the original dataset derived from Mascot but was found to be strongly supported when we re-ran analysis using another protein detection database (Paragon). Identification of linked proteins helps to partially overcome the coverage issue in shotgun proteomics analysis. The set of linked proteins are found to be enriched for cancer-specific processes, and more likely so if they are more highly linked. Additionally, a higher quality linked set is derived if network-cleaning is performed prior. This form of network-based analysis complements the cluster-based approach, and can provide a larger list of proteins on which to perform functional analysis, as well as for biomarker identification.

Downregulation of mitogen-activated protein kinase 1 of Leishmania donovani field isolates is associated with antimony resistance

Emergence of resistance to pentavalent antimonials has become a severe obstacle in the treatment of visceral leishmaniasis (VL) on the Indian subcontinent. The mechanisms operating in laboratory-generated strains are somewhat known, but the determinants of clinical antimony resistance are not well understood. By utilizing a DNA microarray expression profiling approach, we identified a gene encoding mitogen-activated protein kinase 1 (MAPK1) for the kinetoplast protozoan Leishmania donovani (LdMAPK1) that was consistently downregulated in antimony-resistant field isolates. The expression level of the gene was validated by real-time PCR. Furthermore, decreased expression of LdMAPK1 was also confirmed at the protein level in resistant isolates. Primary structure analysis of LdMAPK1 revealed the presence of all of the characteristic features of MAPK1. When expressed in Escherichia coli, the recombinant enzyme showed kinase activity with myelin basic protein as the substrate and was inhibited by staurosporine. Interestingly, overexpression of this gene in a drug-sensitive laboratory strain and a resistant field isolate resulted in increased the sensitivity of the transfectants to potassium antimony tartrate, suggesting that it has a role in antimony resistance. Our results demonstrate that downregulation of LdMAPK1 may be in part correlated with antimony drug resistance in Indian VL isolates.

Neural cell 3D microtissue formation is marked by cytokines' up-regulation

Cells cultured in three dimensional (3D) scaffolds as opposed to traditional two-dimensional (2D) substrates have been considered more physiologically relevant based on their superior ability to emulate the in vivo environment. Combined with stem cell technology, 3D cell cultures can provide a promising alternative for use in cell-based assays or biosensors in non-clinical drug discovery studies. To advance 3D culture technology, a case has been made for identifying and validating three-dimensionality biomarkers. With this goal in mind, we conducted a transcriptomic expression comparison among neural progenitor cells cultured on 2D substrates, 3D porous polystyrene scaffolds, and as 3D neurospheres (in vivo surrogate). Up-regulation of cytokines as a group in 3D and neurospheres was observed. A group of 13 cytokines were commonly up-regulated in cells cultured in polystyrene scaffolds and neurospheres, suggesting potential for any or a combination from this list to serve as three-dimensionality biomarkers. These results are supportive of further cytokine identification and validation studies with cells from non-neural tissue.

Gene expression signatures and ex vivo drug sensitivity profiles in children with acute lymphoblastic leukemia

INTRODUCTION

Causes of treatment failure in acute lymphoblastic leukemia (ALL) are still poorly understood. Microarray technology gives new possibilities for the analysis of the biology of leukemias. We hypothesize that drug sensitivity in pediatric ALL is driven by specific molecular mechanisms that correlate with gene expression profiles assessed by microarray analysis.

OBJECTIVE

The aim of the study was to determine the ex vivo resistance profiles of 20 antileukemic drugs and gene expression profiles, with relation to response to initial therapy.

PATIENTS AND METHODS

Lymphoblasts were analyzed after bone marrow biopsy was obtained from 56 patients. The profile of in vitro resistance to drugs was determined in the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazoliumbromide (MTT) cytotoxicity assay. High-quality total RNA was prepared and hybridized to oligonucleotide arrays HG-U133A 2.0 Chip (Affymetrix). The expression of selected genes was tested by qualitative reverse transcription polymerase chain reaction (qRT-PCR).

RESULTS AND CONCLUSIONS

The exposure of leukemic blasts to drugs initiates a complex cellular response, which reflects global changes in gene expression. Changes in the expression of several genes are highly correlated with drug resistance.

Peripheral blood gene expression profiles in metabolic syndrome, coronary artery disease and type 2 diabetes

To determine if individuals with metabolic disorders possess unique gene expression profiles, we compared transcript levels in peripheral blood from patients with coronary artery disease, type 2 diabetes and their precursor state, metabolic syndrome to those of control subjects and subjects with rheumatoid arthritis. The gene expression profile of each metabolic state was distinguishable from controls and correlated with other metabolic states more than with rheumatoid arthritis. Of note, subjects in the metabolic cohorts over-expressed gene sets that participate in the innate immune response. Genes involved in activation of the pro-inflammatory transcription factor, NF-κB, were over-expressed in coronary artery disease while genes differentially expressed in type 2 diabetes play key roles in T cell activation and signaling. RT-PCR validation confirmed microarray results. Furthermore, several genes differentially expressed in human metabolic disorders have been previously shown to participate in inflammatory responses in murine models of obesity and Type 2 diabetes. Taken together, these data demonstrate that peripheral blood from individuals with metabolic disorders display overlapping and non-overlapping patterns of gene expression indicative of unique, underlying immune processes.

Relapse of acute lymphoblastic leukemia in children in the context of microarray analyses

Over the last four decades the treatment of patients with newly diagnosed childhood acute lymphoblastic leukemia (ALL) has improved remarkably. However, still about 20% of children with ALL relapse despite risk-adapted polychemotherapy. The prognosis of relapsed ALL is relatively poor, even with modern aggressive chemotherapy. Identification of the biological and genetic mechanisms contributing to recurrence in patients with ALL is critical for the development of effective therapeutic strategies to treat refractory leukemic patients. Allogeneic hematopoietic stem-cell transplantation is the treatment of choice for many children with relapsed ALL. The gene expression profile obtained by microarray technology could provide important determinants of the drug response and clinical outcome in childhood ALL. Incorporation of the data on expression levels of newly identified genes into existing strategies of risk stratification might improve clinical management. Current microarray data show correlation of in vitro drug resistance with significant patterns of gene expression and explain clinical differences between early and late relapse. Genes involved in cell proliferation, self-renewal and differentiation, protein biosynthesis, carbohydrate metabolism, and DNA replication and repair are usually among those highly expressed in relapsed lymphoblasts. Current status and future perspectives of microarray data on gene expression and drug resistance profile in relapsed pediatric ALL are discussed in this review.

KLF4 promotes hydrogen-peroxide-induced apoptosis of chronic myeloid leukemia cells involving the bcl-2/bax pathway

K562 cells and peripheral blood mononuclear cells were treated with hydrogen peroxide (H(2)O(2)) to determine the expression of Krüppel-like factor (KLF) 4. A full-length complementary DNA or an anti-sense oligonucleotide of KLF4 was transfected into cells, and expressions of B-cell lymphoma/leukemia-2 (bcl-2) and bcl-2-associated X (bax) proteins were analyzed. The results showed that H(2)O(2) treatment of cells resulted in an increase in KLF4 levels; KLF4 induced apoptosis and slowed cell growth, potentially resulting from up-regulation of bax and down-regulation of bcl-2. Transcriptional activities on bcl-2 and bax were promoted following KLF4 overexpression potentially through KLF4 binding sites on corresponding promoters. All results indicate that KLF4 induces apoptosis in leukemia cells involving the bcl-2/bax pathway during H(2)O(2) stimulation, suggesting a potential mechanism for research on drug-induced apoptosis.

Promoter polymorphisms in the β-2 adrenergic receptor are associated with drug-induced gene expression changes and response in acute lymphoblastic leukemia

We investigated whether genetic polymorphisms in the promoter region of the proapoptotic β-2 adrenergic receptor gene (ADRB2) influence treatment-induced changes in ADRB2 expression in leukemia cells and response to chemotherapy. The ADRB2 promoter region was genotyped in germline DNA from 369 children with acute lymphoblastic leukemia (ALL). For 95 of the patients, sufficient RNA was available before and after in vivo treatment to assess treatment-induced gene expression changes in ALL cells. After treatment, the median ADRB2 mRNA expression was ninefold lower in leukemia cells of patients who ultimately relapsed as compared with patients who remained in continuous complete remission (CCR). Polymorphisms in the ADRB2 promoter were significantly linked to methotrexate (MTX)-induced upregulation in ADRB2 gene expression in ALL cells. Moreover, the ADRB2 promoter haplotype was significantly related to early treatment response in 245 children with ALL who received uniform treatment. We conclude that germline polymorphisms in ADRB2 are linked to the antileukemic effects of ALL chemotherapy.

Detection of doxorubicin-induced apoptosis of leukemic T-lymphocytes by laser tweezers Raman spectroscopy

Laser tweezers Raman spectroscopy (LTRS) was used to acquire the Raman spectra of leukemic T lymphocytes exposed to the chemotherapy drug doxorubicin at different time points over 72 hours. Changes observed in the Raman spectra were dependent on drug exposure time and concentration. The sequence of spectral changes includes an intensity increase in lipid Raman peaks, followed by an intensity increase in DNA Raman peaks, and finally changes in DNA and protein (phenylalanine) Raman vibrations. These Raman signatures are consistent with vesicle formation, cell membrane blebbing, chromatin condensation, and the cytoplasm of dead cells during the different stages of drug-induced apoptosis. These results suggest the potential of LTRS as a real-time single cell tool for monitoring apoptosis, evaluating the efficacy of chemotherapeutic treatments, or pharmaceutical testing.